Bio-Inspired Multi-UAV Path Planning Heuristics: A Review
<jats:p>Despite the rapid advances in autonomous guidance and navigation techniques for unmanned aerial vehicle (UAV) systems, there are still many challenges in finding an optimal path planning algorithm that allows outlining a collision-free navigation route from the vehicle’s current positi...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024
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| Online Access: | https://hdl.handle.net/1721.1/153607 |
| _version_ | 1826209373100179456 |
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| author | Aljalaud, Faten Kurdi, Heba Youcef-Toumi, Kamal |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Aljalaud, Faten Kurdi, Heba Youcef-Toumi, Kamal |
| author_sort | Aljalaud, Faten |
| collection | MIT |
| description | <jats:p>Despite the rapid advances in autonomous guidance and navigation techniques for unmanned aerial vehicle (UAV) systems, there are still many challenges in finding an optimal path planning algorithm that allows outlining a collision-free navigation route from the vehicle’s current position to a goal point. The challenges grow as the number of UAVs involved in the mission increases. Therefore, this work provides a comprehensive systematic review of the literature on the path planning algorithms for multi-UAV systems. In particular, the review focuses on biologically inspired (bio-inspired) algorithms due to their potential in overcoming the challenges associated with multi-UAV path planning problems. It presents a taxonomy for classifying existing algorithms and describes their evolution in the literature. The work offers a structured and accessible presentation of bio-inspired path planning algorithms for researchers in this subject, especially as no previous review exists with a similar scope. This classification is significant as it facilitates studying bio-inspired multi-UAV path planning algorithms under one framework, shows the main design features of the algorithms clearly to assist in a detailed comparison between them, understanding current research trends, and anticipating future directions. Our review showed that bio-inspired algorithms have a high potential to approach the multi-UAV path planning problem and identified challenges and future research directions that could help improve this dynamic research area.</jats:p> |
| first_indexed | 2024-09-23T14:21:29Z |
| format | Article |
| id | mit-1721.1/153607 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:21:29Z |
| publishDate | 2024 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | mit-1721.1/1536072024-03-01T03:22:53Z Bio-Inspired Multi-UAV Path Planning Heuristics: A Review Aljalaud, Faten Kurdi, Heba Youcef-Toumi, Kamal Massachusetts Institute of Technology. Department of Mechanical Engineering <jats:p>Despite the rapid advances in autonomous guidance and navigation techniques for unmanned aerial vehicle (UAV) systems, there are still many challenges in finding an optimal path planning algorithm that allows outlining a collision-free navigation route from the vehicle’s current position to a goal point. The challenges grow as the number of UAVs involved in the mission increases. Therefore, this work provides a comprehensive systematic review of the literature on the path planning algorithms for multi-UAV systems. In particular, the review focuses on biologically inspired (bio-inspired) algorithms due to their potential in overcoming the challenges associated with multi-UAV path planning problems. It presents a taxonomy for classifying existing algorithms and describes their evolution in the literature. The work offers a structured and accessible presentation of bio-inspired path planning algorithms for researchers in this subject, especially as no previous review exists with a similar scope. This classification is significant as it facilitates studying bio-inspired multi-UAV path planning algorithms under one framework, shows the main design features of the algorithms clearly to assist in a detailed comparison between them, understanding current research trends, and anticipating future directions. Our review showed that bio-inspired algorithms have a high potential to approach the multi-UAV path planning problem and identified challenges and future research directions that could help improve this dynamic research area.</jats:p> 2024-02-29T13:24:38Z 2024-02-29T13:24:38Z 2023 2024-02-29T13:22:09Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/153607 Aljalaud, Faten, Kurdi, Heba and Youcef-Toumi, Kamal. 2023. "Bio-Inspired Multi-UAV Path Planning Heuristics: A Review." Mathematics, 11 (10). en 10.3390/math11102356 Mathematics Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf MDPI AG MDPI |
| spellingShingle | Aljalaud, Faten Kurdi, Heba Youcef-Toumi, Kamal Bio-Inspired Multi-UAV Path Planning Heuristics: A Review |
| title | Bio-Inspired Multi-UAV Path Planning Heuristics: A Review |
| title_full | Bio-Inspired Multi-UAV Path Planning Heuristics: A Review |
| title_fullStr | Bio-Inspired Multi-UAV Path Planning Heuristics: A Review |
| title_full_unstemmed | Bio-Inspired Multi-UAV Path Planning Heuristics: A Review |
| title_short | Bio-Inspired Multi-UAV Path Planning Heuristics: A Review |
| title_sort | bio inspired multi uav path planning heuristics a review |
| url | https://hdl.handle.net/1721.1/153607 |
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